1. Field of the Invention
The present invention relates to an image processing technology for newly producing an image by sparse coding from a given image.
2. Description of the Related Art
Various image processes are performed using a technology of subtracting, from a pixel value distribution in an arbitrary partial region of a known image, an average pixel value (DC component) in the partial region to acquire a component (AC component) and of converting the AC component into an AC component in a partial region of an unknown image corresponding to the partial region of the known image.
For example, Michael Elad, Michal Aharon, “Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries”, Transactions on Image Processing, U.S.A., IEEE, 2006, Vol. 15, Issue 12, pp. 3736-3745, which is hereinafter referred to as Literature 1, discloses an image processing method capable of performing a noise removal process which produces an original image including no noise from a degraded image including the noise. Specifically, the method first estimates, from an AC component in a small region (hereinafter, referred to as “an extraction region”) arbitrarily extracted in the degraded image, an AC component including no noise in a small region (hereinafter, referred to as “a corresponding region”) in the original image corresponding to the extraction region. Next, the method adds together a DC component in the extraction region of the degraded image and the estimated AC component to estimate a pixel value distribution in the corresponding region of the original image. The method performs the above processes on an entire degraded image to produce the original image in which the noise is removed.
Jianchao Yang, Zhaowen Wang, Zhe Lin, Scott Cohen, Thomas Huang, “Couple Dictionary Training for Image Super-Resolution”, Transactions on Image Processing, U.S.A., IEEE, 2012, Vol. 21, Issue 8, pp. 3467-3478, which is hereinafter referred to as Literature 2, discloses an image processing method capable of performing super-resolution processing of acquiring, from a low resolution image (degraded image) produced by performing degradation processing such as decimation of pixels on a high resolution image, a high resolution image equivalent to that before the degradation processing. Specifically, the method first performs interpolation processing by a nearest neighbor method or the like on the low resolution image to produce an intermediate image having a high resolution. Since this intermediate image is smoothed through the interpolation processing, the method estimates, an AC component in an arbitrary extraction region of the intermediate image, an unsmoothed AC component in a corresponding region of the high resolution image. Next, the method adds together a DC component in the extraction region of the intermediate image and the estimated AC component to estimate a pixel value distribution in the corresponding region of the high resolution image. The method performs the above processes on an entire intermediate image to produce a high resolution image subjected to the super-resolution processing. The image processing methods disclosed in Literatures 1 and 2 each use bases previously produced by dictionary learning from the AC components in multiple small regions extracted from training images before and after their degradation. Such image processing methods are each called “a sparse representation-based image processing method, or “sparse coding” to be used in the following description. The basis is a set of elements as the small regions produced by dictionary learning. The training image is an image for producing the basis by dictionary learning.
The sparse coding disclosed in Literatures 1 and 2 is based on an assumption that the DC component in the extraction region of the degraded image and the intermediate image which are each an input image is equal to the DC component in the corresponding region of the original image and the high resolution image which are each an output image. Thus, when this assumption holds, the output image can be produced from the input image accurately.
However, this assumption does not hold in many cases. For example, these cases include a case of performing a color conversion of an image of a pathological sample stained with a certain color into an image of a pathological sample stained with another color and a case of calculating, from a sample image of an unknown sample captured through a partially coherent imaging system, a complex amplitude distribution of light transmitted through the sample. In these cases, the DC component in the extraction region of the input image differs from the DC component in the corresponding region of the output image, so that the sparse coding disclosed in Literatures 1 and 2 cannot be directly applied thereto.